SHAPE Summer Session 2, Machine Learning & Artificial Intelligence

Summer High School Academic Program for Engineers

Columbia University, Summer 2025

AI systems like ChatGPT, other language models, and computer vision systems are becoming a big part of our everyday lives. But how do these systems actually work? What can they do, and where do they fall short?

This crash course in machine learning and AI will introduce you to the basics. You’ll start with simpler machine learning models, learn about neural networks and image classification models, and explore how advanced models like ChatGPT are built and trained.

Who is this course for? Students who are comfortable with basic coding in any language (like Java or Python).

Teaching Staff

Instructor: Daniel Bauer
Summer Student Leaders: Batu Yeltekin, Michael Del Toro, Leo Zubarev

Location: Botwinick Lab - Mudd 1224

What you Will Learn

• A quick review of Python
• Important math concepts: basic calculus and working with vectors and matrices
• How to handle different types of data, including tables, images, and text
• How neural networks work and learn from data
• How to classify images using Convolutional Neural Networks
• How to represent words in a way that computers understand
• How transformers (a type of ML model) work
• The basics of models like GPT
• How pretraining and fine-tuning make these models smarter
• How to interact with language models using prompts and API calls

Class Format: Each day, we’ll have short lectures and work on real hands-on projects in small groups.

A typical day:

Time	Session
9:00 AM	Check-in
9:15 AM	Morning session (workshops, speakers, or project time)
10:15 AM	Professor-led class and lab time
12:15 PM	Lunch Break
1:30 PM	Afternoon check-in
1:45 PM	Professor-led class and project time (SSLs available, Professor Bauer available until 3pm)
4:00 PM	Elective or Community Hour
5:00 PM	Dismissal

The following schedule is tentative and subject to change: 

Date	Topic/Materials	Exercises/Projects
Mon 7/28	Welcome and Introduction / Course overview.  Python review if necessary.	Codio setup Game playing warmup challenge
Tue 7/29	Introduction to Supervised Machine Learning	Loading and exploring a tabular dataset with pandas and matplotlib Training a classifier with scikit-learn
Wed 7/30	Biological and artificial neurons. Linear models and the Perceptron algorithm.	Perceptron by hand. Implement the Perceptron algorithm and run it on a linearly classifiable tabular dataset. Visualize the separation boundary.
Thu 7/31	Vectors and matrices in numpy. Vector and matrix multiplication.	(vector, matrix, numpy exercises. Working with images in numpy / matplotlib.)[https://github.com/daniel-bauer/shape-ml-ai/blob/main/day04/day4-exercises.ipynb]
Fri 8/1	Neural Network basics (forward activation only). Computation graphs and activation functions.	Build a mini neural network from scratch in numpy (forward pass only).
Mon 8/4	Calculus basics. Chain rule. Logistic regression. Backpropagation.	Extend the mini neural net from last week to include backprop. Optionally, train your NN on the penguin data set.
Tue 8/5	pyTorch	Use pytorch with autograd to build a NN to classify the MNIST data (handwritten digit recognition)
Wed 8/6	-	wrap-up Mon&Tue work (backpropagation implementation and MNIST classifier in pyTorch)
Thu 8/7	Convolutional Neural Networks and Image Classification	Build a CNN in pyTorch and train it on the CIFAR 10 and CIFAR 100 data.
Fri 8/8	ResNet	Try ResNet on your own images Use chatGPT to write code for a simple web interface to upload and classify images (extension: classify live webcam images).
Mon 8/11	Pretraining/Fine-tuning with ResNet	Fine-tune ResNet on the pet or food dataset.
Tue 8/12	Neural Language Models and Embeddings	project
Wed 8/13	-	project
Thu 8/14	Attention and Transformers	project
Fri  8/15	Symposium	project

Project: 

Most of the last week will be spent working on a project and/or preparing a presentation for final day symposium.